Topic outline

  • General

    Lecturer: Joni Virta. Head assistant: Paavo Raittinen.

  • Lectures

    The course lectures are on Thursdays 10.01.-14.02. and 28.02.-04.04. at 12.15-14.00 in the lecture hall C, Kandidaattikeskus (Otakaari 1).

    The course has a total of 12 lectures with the following topics:

    1. Descriptive statistics
    2. Confidence intervals and hypothesis testing
    3. Non-parametric tests
    4. Inference for binary data
    5. Distribution tests
    6. Correlation and dependency

    -----------Exam week break-----------

    1. Linear regression I
    2. Guest lecture
    3. Linear regression II
    4. Linear regression III
    5. ANOVA
    6. Summary lecture

  • Assignments

  • Materials

  • For Aalto users

    Not available unless: Your User account contains (use: aalto.fi) contains aalto.fi

  • Project work

    The project work consists of analyzing a research problem and documenting this process in a report. The project work must be done individually and not in groups.

    Anyone can do the project work as a substitute for the exam, regardless of whether you attended the course actively or just planned to take the exam.


    Instructions regarding the report:

    The report can contain text, code (if relevant), tables and plots and must be either .pdf or .doc file.

    The project work can be done with any software, it does not have to be R.

    Please return your report to MyCourses at https://mycourses.aalto.fi/course/view.php?id=22047&section=5 before 23:55 at 28.04 (no late submissions are allowed).


    Your report should contain descriptions of each of the following 12 steps. Please use the same numbering in your report.

    1.       Choose a data set you wish to study and explain in plain English what it is about. For finding a suitable data set you can use, for example, the following list which details different data sets found in various R-packages: https://vincentarelbundock.github.io/Rdatasets/datasets.html

    2.       Formulate and present a research question.

    3.       Provide numerical descriptive statistics of your data.

    4.       Provide visual descriptive statistics of your data

    5.       Based on the descriptive statistics, choose a suitable method to analyze your research question.

    6.       State your research question “mathematically” (i.e., as a pair of hypotheses or as a statement about some model parameter).

    7.       Shortly describe the assumptions of your method.

    8.       Analyze the data using your chosen method and provide the method output as given by your software.

    9.       Check whether the assumptions of your chosen method are fulfilled.

    10.   State your conclusions “mathematically” (in terms of the model parameters and hypotheses).

    11.   State your conclusions in plain English (as if communicating them to a non-expert).

    12.   Discuss one of the following: how could the analysis be continued afterwards / what would have been some alternative methods of conducting the analysis.


    Each of the twelve steps will be graded on a scale 0-2 as follows:

    ·         0 points = step not done

    ·         1 point = step done

    ·         2 points = step done well

    The maximum amount of points is 24 (this substitutes for an exam), on top of which we will still add the homework points you have obtained.


    The recommended maximum length of the report is 5 pages (a rough guideline that should be read as "it is not necessary to write overly long reports". But it is perfectly acceptable to go beyond 5 pages if you so wish.)